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Move it! How an Electric Contest Motivates Households to Shift their Load Profile

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  • Sylvain Weber
  • Stefano Puddu
  • Diana Pacheco

Abstract

Photovoltaic systems generate electricity around noon, when many homes are empty. Conversely, residential electricity demand peaks in the evening, when production from solar sources is impossible. Based on a randomized control trial, we assess the effectiveness of alternative demand response measures aimed at mitigating these imbalances. More precisely, through information feedback and financial rewards, we encourage households to shift electricity consumption toward the middle of the day. Using a difference-in-differences approach, we find that financial incentives induce a significant increase of the relative consumption during the period of the day when most solar radiation takes place. Information feedback, however, pushes households to decrease overall consumption, but induces no load shifting.

Suggested Citation

  • Sylvain Weber & Stefano Puddu & Diana Pacheco, 2016. "Move it! How an Electric Contest Motivates Households to Shift their Load Profile," IRENE Working Papers 16-03, IRENE Institute of Economic Research.
    • Handle: RePEc:irn:wpaper:16-03
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    2. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Sylwia Słupik & Joanna Kos-Łabędowicz & Joanna Trzęsiok, 2021. "How to Encourage Energy Savings Behaviours? The Most Effective Incentives from the Perspective of European Consumers," Energies, MDPI, vol. 14(23), pages 1-25, November.
    4. Matteo Caldera & Asad Hussain & Sabrina Romano & Valerio Re, 2023. "Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform," Energies, MDPI, vol. 16(2), pages 1-23, January.
    5. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Optimal energy management in all-electric residential energy systems with heat and electricity storage," Applied Energy, Elsevier, vol. 254(C).
    6. Yilmaz, S. & Weber, S. & Patel, M.K., 2019. "Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes," Energy Policy, Elsevier, vol. 133(C).
    7. Brännlund, Runar & Vesterberg, Mattias, 2021. "Peak and off-peak demand for electricity: Is there a potential for load shifting?," Energy Economics, Elsevier, vol. 102(C).
    8. Wang, Peiguang & Zhang, Zhaoyan & Fu, Lei & Ran, Ning, 2021. "Optimal design of home energy management strategy based on refined load model," Energy, Elsevier, vol. 218(C).
    9. Andreolli, Francesca & D’Alpaos, Chiara & Moretto, Michele, 2022. "Valuing investments in domestic PV-Battery Systems under uncertainty," Energy Economics, Elsevier, vol. 106(C).
    10. Shen, Meng & Li, Xiang & Lu, Yujie & Cui, Qingbin & Wei, Yi-Ming, 2021. "Personality-based normative feedback intervention for energy conservation," Energy Economics, Elsevier, vol. 104(C).
    11. Azarova, Valeriya & Cohen, Jed J. & Kollmann, Andrea & Reichl, Johannes, 2020. "Reducing household electricity consumption during evening peak demand times: Evidence from a field experiment," Energy Policy, Elsevier, vol. 144(C).
    12. Spandagos, Constantine & Baark, Erik & Ng, Tze Ling & Yarime, Masaru, 2021. "Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    13. Bernadeta Gołębiowska & Anna Bartczak & Wiktor Budziński, 2019. "Impact of social comparison on DSM in Poland," Working Papers 2019-10, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    electricity usage; solar energy; demand response; randomized control trial; smart metering.;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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